Evaluating Cost Savings Associated with a Patient-Facing Asthma Mobile Health Intervention
Bibliographic record
Abstract
The patient-facing ASTHMAXcel mobile platform has been linked to improved asthma knowledge decreased asthma-related health care utilization (emergency department [ED] visits, hospitalizations), and reduced prednisone use among adult and pediatric patient populations.Given the upfront costs associated with developing mobile health platforms, this paper seeks to estimate the savings attributable to pediatric and adult users of the ASTHMAXcel platform through decreased hospitalizations, ED visits, and prednisone use.Forty adult patients and 39 pediatric patients with asthma completed studies evaluating the effectiveness of the ASTHMAXcel mobile platform at Montefiore Medical Center. Study participants received the mobile intervention for 6 months and questionnaires were administered at baseline and follow-up visits. Using internal cost data from Montefiore and information on insurance coverage, we estimated the average costs saved by the decreases in ED visits, hospitalizations, and prednisone usage throughout the study time period.Throughout the study time period, participant exposure to ASTHMAXcel and ASTHMAXcel adventures was estimated to save a total of $367,280 with $270,732 in savings from adults' decreased asthma-related health care utilization and $96,548 from pediatric decreases in asthma-related health care utilization. The bulk of these savings estimates are driven by decreases in hospitalizations among both pediatric and adult participants during the study time frame.Given the significant economic burden of asthma treatment and management and its disproportionate impact on those who are economically disadvantaged, it is critical that asthma interventions are assessed not only for their clinical effectiveness but also for their cost-effectiveness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".